Sun Yifei, He Xuming, Hu Jianhua
Department of Biostatistics, Columbia University.
Department of Statistics, University of Michigan.
Ann Appl Stat. 2022 Dec;16(4):2266-2278. doi: 10.1214/21-AOAS1589. Epub 2022 Sep 26.
Late-stage clinical trials have been conducted primarily to establish the efficacy of a new treatment in an intended population. A corollary of population heterogeneity in clinical trials is that a treatment might be effective for one or more subgroups, rather than for the whole population of interest. As an example, the phase III clinical trial of panitumumab in metastatic colorectal cancer patients failed to demonstrate its efficacy in the overall population, but a subgroup associated with tumor KRAS status was found to be promising (Peeters et al. ( 28 (2010) 4706-4713)). As we search for such subgroups via data partitioning based on a large number of biomarkers, we need to guard against inflated type I error rates due to multiple testing. Commonly-used multiplicity adjustments tend to lose power for the detection of subgroup treatment effects. We develop an effective omnibus test to detect the existence of, at least, one subgroup treatment effect, allowing a large number of possible subgroups to be considered and possibly censored outcomes. Applied to the panitumumab trial data, the proposed test would confirm a significant subgroup treatment effect. Empirical studies also show that the proposed test is applicable to a variety of outcome variables and maintains robust statistical power.
晚期临床试验主要是为了确定一种新疗法在目标人群中的疗效。临床试验中人群异质性的一个必然结果是，一种治疗方法可能对一个或多个亚组有效，而不是对整个感兴趣的人群有效。例如，帕尼单抗在转移性结直肠癌患者中的III期临床试验未能在总体人群中证明其疗效，但发现一个与肿瘤KRAS状态相关的亚组很有前景（Peeters等人，《临床肿瘤学杂志》28(2010)4706 - 4713）。当我们通过基于大量生物标志物的数据划分来寻找这些亚组时，我们需要防范由于多次检验导致的I型错误率膨胀。常用的多重性调整往往会降低检测亚组治疗效果的功效。我们开发了一种有效的综合检验方法，以检测至少一个亚组治疗效果的存在，允许考虑大量可能的亚组以及可能被删失的结果。应用于帕尼单抗试验数据时，所提出的检验将证实存在显著的亚组治疗效果。实证研究还表明，所提出的检验适用于各种结果变量，并保持强大的统计功效。
